linter

tests/conftest.py

@@ -3,8 +3,8 @@
 import os
 import sys
 
-import pandas as pd
 import numpy as np
+import pandas as pd
 import pytest
 
 _TEST_FOLDER = os.path.dirname(__file__)
@@ -42,7 +42,8 @@ def random_data_no_weights(get_fixture):
 def random_data_weights(get_fixture):
     return get_fixture("random_data_n100_p5_12549_weights.csv")
 
+
 @pytest.fixture(scope="session")
 def rng():
     seed = 8232
-    return np.random.default_rng(seed=seed)
+    return np.random.default_rng(seed=seed)

tests/test_linear_solver.py

@@ -4,6 +4,7 @@
 from elexsolver.LinearSolver import LinearSolver
 from elexsolver.QuantileRegressionSolver import QuantileRegressionSolver
 
+
 def test_fit():
     solver = LinearSolver()
     with pytest.raises(NotImplementedError):
@@ -23,6 +24,5 @@ def test_residuals_without_weights(rng):
     reg.fit(x, y, fit_intercept=False)
     reg.predict(x)
 
-    residuals_train = reg.residuals(x, y, K=None, center=False)
-    residuals_K = reg.residuals(x, y, K=10, center=False)
-    import pdb; pdb.set_trace()
+    reg.residuals(x, y, K=None, center=False)
+    reg.residuals(x, y, K=10, center=False)

tests/test_ols.py

@@ -30,6 +30,7 @@ def test_basic2():
     preds = lm.predict(x)
     assert all(np.abs(preds - [6.666667, 6.666667, 6.666667, 15]) <= TOL)
 
+
 def test_cache():
     lm = OLSRegressionSolver()
     x = np.asarray([[1, 1], [1, 1], [1, 1], [1, 2]])
@@ -45,6 +46,7 @@ def test_cache():
     assert lm.hat_vals is not None
     assert lm.coefficients is not None
 
+
 ######################
 # Intermediate tests #
 ######################

tests/test_quantile.py

@@ -55,6 +55,7 @@ def test_basic_upper():
     preds = quantreg.predict(x)
     np.testing.assert_array_equal(preds, [[9], [9], [9], [15]])
 
+
 def test_cache():
     quantreg = QuantileRegressionSolver()
     tau = 0.9
@@ -67,6 +68,7 @@ def test_cache():
     quantreg.fit(x, y, tau, cache=True)
     assert len(quantreg.coefficients) > 0
 
+
 ######################
 # Intermediate tests #
 ######################